Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics

Authors

Zhuohang Li
Chao Yan
Xinmeng Zhang
Gharib Gharibi
Zhijun Yin
Xiaoqian Jiang
Bradley A Malin

Publication Date

1-1-2023

Journal

AMIA Annual Symposium Proceedings

Abstract

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

Keywords

Humans, Deep Learning, Medical Informatics, Electronic Health Records, Privacy

PMID

38222326

PMCID

PMC10785879

PubMedCentral® Posted Date

1-11-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes